There exist various service systems that have HIERARCHICAL structure. In HIERARCHICAL service networks, facilities at different levels provide different types of services. For example, in health care systems, general centers provide low-level services such as primary health care services, while the specialized hospitals provide high-level services. Because of demand congestion in service networks, location of servers and allocation of demand nodes have a strong impact on the length of queues at servers as well as on the response times to service calls. The thrust of this article is the development of HIERARCHICAL location-allocation MODELS for congested systems by employing queueing theory in a fuzzy framework. The new MODELS allow partial coverage of demand nodes and approximate determination of parameters. Using queueing theory and fuzzy conditions, both referral and nested HIERARCHICAL MODELS are developed for the maximal covering location problem (MCLP). An example is solved for both an existing probabilistic model and the new fuzzy MODELS and the results are compared.

Hub facility location problems involve the establishment of strategic hub facilities and the allocation of demand nodes to them. Meanwhile, with the emergence of new transport and distribution networks with multi-level structures, the design of such networks has evolved. The diversity of transportation system has added multimodality to these problems. Considering the strategic nature and long-term implications of decisionmaking in this  eld, the decisions shall be of high reliability. The present study is an attempt to consider the reliability of communication paths in proportion to the volume of transactions through them while covering all of the above-mentioned issues in hub location problems at the same time. Incorporating HIERARCHICAL characteristics of the problem into the model, one can obtain a signi cantly enhanced model in terms of multimodality and reliability. A Lagrangian solution method was developed considering the strategic level of the problem and the importance of the solution accuracy. The model was then validated in terms of time and quality.

Semantic network approaches view organization or representation of internal lexicon in the form of either spreading or HIERARCHICAL system identified, respectively, as Spreading Activation Model (SAM) and HIERARCHICAL Network Model (HNM). However, the validity of either model is amongst the intact issues in the literature which can be studied through basing the instruction compatible with the principles of each model. In a bid to fill this gap, this study was designed to empirically verify the effectiveness of SAM compared to HNM in both developing and retention rate of vocabulary knowledge. To this end, 67 Iranian EFL learners were divided into two experimental groups (34 and 33) and one was exposed to HNMbased while the other to SAM-based vocabulary instruction for 10 sessions. In the light of groupcomparison experimental design, the participants' both immediate achievement and long-term storage were measured through an immediate and a delayed post-tests, respectively. The parametric statistical analyses showed that the group being exposed to HNM-based instruction outperformed the other group in both the immediate and delayed post-tests. The findings bear two distinct messages: yielding support to more validity of HNM as a model of internal lexicon organization and supporting the educational implications of cognitively compatible instruction of language components.

In this paper, we first introduce semi-parametric heteroscedastic HIERARCHICAL MODELS. Then, we define a new version of the empirical likelihood function (Restricted Joint Empirical likelihood) and use it to obtain the shrinkage estimators of the MODELS' parameters in these MODELS. Under different assumptions, a simulation study investigates the better performance of the restricted joint empirical likelihood function in the analysis of semi-parametric heterogeneity HIERARCHICAL MODELS. Furthermore, we analyze an actual data set using the RJEL method.

In HIERARCHICAL service networks, facilities at different levels provide different types of service. For example, in health care systems, general centers provide low-level services, such as primary health care, while specialized hospitals provide high-level services. Because of the demand congestion at service networks, the location of servers and their allocation of demand nodes can have a strong impact on the length of the queue at each server, as well as on the response time to service calls. This study attempts to develop HIERARCHICAL location-allocation MODELS for congested systems by employing a queuing theory in a fuzzy framework. The parameters of each model are approximately evaluated and stated as fuzzy-numbers. The coverage of demand nodes is also considered in an approximate manner and is stated by the degree of membership. Using queuing theory and fuzzy conditions, both referral and nested HIERARCHICAL MODELS are developed for the Location Set Covering Problem (LSCP). To demonstrate the performance of the proposed MODELS, a numerical example is solved in order to compare the results obtained from the existing probabilistic MODELS and the new fuzzy MODELS developed in this paper.

‎Quantile regression model and its generalizations‎, ‎including M-quantile regression model‎, ‎are analyzed usually via a nonparametric approach and their parameters are estimated using some iterative optimization algorithms‎. ‎For these reason‎, ‎in these MODELS confidence intervals and hypotheses testing have done perforce using rank-based or bootstrapping approaches‎. ‎In this paper‎, ‎we consider parametric analysis of M-quantile model‎. ‎It is shown that‎, ‎the frequentist based approach of maximum likelihood estimation leads to results that are similar to the nonparametric approach‎. ‎Hence‎, ‎in order to achieve a more afficient model‎, ‎we have been used the Bayes theory and a‎ ‎HIERARCHICAL Bayes model has been developed‎. ‎The efficiency of the proposed model has been assessed via a simulation study and real word example‎. The ‎results ‎show ‎that ‎the ‎Bayesian ‎approach of ‎m-quantile ‎regression ‎analysis ‎is ‎more ‎efficient ‎than ‎the correspond ‎frequantist ‎appro‎ach, for all sample sizes. In addition, the proposed model truly takes into account the effect of the outlier observation, which causes skewness in response variable distribution, in modeling.

In this paper, conventional SAPO-34 and Cerium HIERARCHICAL SAPO-34 zeolites are synthesized using a hydrothermal method for methanol-to-propylene (MTP) reaction. The Ce-H-SAPO-34 catalyst shows a slightly larger crystal size, bimodal pore size distribution (microporous and mesoporous) and lower surface acidity compared to conventional SAPO-34 catalysts. According to these physicochemical properties and the previously suggested reaction mechanisms on the conventional SAPO-34 catalyst, a new reaction network is introduced for Ce-H-SAPO-34 catalyst. This was based on the data collected from a catalytic micro reactor in the temperature range of 390-450° C and at atmospheric pressure. The lumped kinetic model and the reaction rate expression have been introduced by considering the reaction network. The parameters were then estimated based on the experimental data using genetic algorithms. Comparing the experiments with the predicted data suggests a good correlation between the experimental data and the model.

Introduction: Because of the ever-increasing human population and the limited quantity of resources that are currently accessible, it is imperative that these resources be used as effectively as possible. The Sistan region experiences a significant lot of hardship in terms of the availability of water and soil that are suited for agricultural purposes (Wang et al., 2015). The elimination of discrimination between different regions, the guaranteeing of food security, and the lessening of the severity of deprivation among the population of the world are some of the key objectives of sustainable agriculture in the context of environmental protection (Shokati and Feizizadeh, 2019). The cultivation of medicinal plants such as isabgol, which have a low requirement for moisture, a high tolerance to drought, and a high value added, are key solutions for dealing with a multitude of environmental, economic, and social constraints in the Sistan region (Asgharipour and Rafiei, 2010). Using a method known as spatial analysis, the purpose of this study was to determine the agro-ecological zoning of the isbgol plant in the Sistan region in order to identify potential areas for the cultivation of this plant in the Sistan region. This was accomplished in order to fulfill the objective of this research. Materials and methods: In the course of this research, the climatic, topographic, and hydrological layers of the area were analyzed. The appropriate planting areas were zoned using a Geographic Information System (GIS) technique that consisted of Boolean MODELS and HIERARCHICAL Analysis Processing (AHP). In this study, equivalence maps were created with Arc/GIS 10, and then the twodimensional matrix of each layer was mapped in order of importance using the Idrisi software. Finally, the degree of importance of each component was calculated. At the very end of the process, final maps were produced by combining existing maps in Arc/GIS 10. Results and discussion: According to the findings, only 11 percent of the total arable land area is suitable for the cultivation of isabgol, while 42 percent is suitable, 42 percent is moderately suitable, 31 percent is marginally suitable, and 14 percent is not suitable at all. According to the final map, the areas of the Sistan area that have the greatest potential for isabgol cultivation include the northern parts of the area, as well as parts of the west and southwest, and a small portion of the northeast of the region. Isabgol could be grown successfully in the majority of Zabol's landscapes thanks to the region's favorable growing conditions. However, there are areas of the northwest and a portion of the southwest and northeast, as well as scattered parts of the west and southwest and a portion of the northeast, that are not appropriate for the cultivation of isabgol. Due to the high precision of the Boolean model and the inherent limitations of the AHP model in this respect, the Boolean model also provides better results than the AHP model. Therefore, it is possible to draw the conclusion that the final map that was derived from the Boolean model is very accurate and that it can be used for locating suitable land areas for isabgol in arid and semiarid regions of Iran. Conclusion: It is possible to apply feasibility assessment MODELS for the aim of management awareness in order to plan appropriately and to maintain environmental sustainability. This can be accomplished through the utilization of management awareness. These MODELS are used to identify medicinal plants that are capable of thriving in each region and are suited for cultivation there. In addition, prospective land-based planning is the best alternative for averting the ongoing crises and lessening the impacts on the environment. This is because prospective land-based planning is focused on looking forward.